
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							from .features import FeatureType


__SCREAMING_SNAKE_CASE						       =						False, False, False





@dataclass
class 							lowerCAmelCase_						:




			'''simple docstring'''


			_lowercase      =							None
			_lowercase      =							True
			_lowercase      =							True
			_lowercase      =							None
			# Automatically constructed
			_lowercase      =							"dict"
			_lowercase      =							pa.struct({'bytes': pa.binary(), 'path': pa.string()}							)
			_lowercase      =							field(default='Audio'			,		init=_A			,		repr=_A							)







			def __call__(	self  ):
										return self.pa_type







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase  ):
										try:
																	import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
										except ImportError as err:
																	raise ImportError('To support encoding audio data, please install \'soundfile\'.'  ) from err
										if isinstance(snake_case_    ,							snake_case_  ):
																	return {"bytes": None, "path": value}
										elif isinstance(snake_case_    ,							snake_case_  ):
																	return {"bytes": value, "path": None}
										elif "array" in value:
																	# convert the audio array to wav bytes
																	SCREAMING_SNAKE_CASE_	:							List[Any]											=BytesIO()
																	sf.write(snake_case_    ,							value['array']    ,							value['sampling_rate']    ,							format='wav'  )
																	return {"bytes": buffer.getvalue(), "path": None}
										elif value.get('path'  ) is not None and os.path.isfile(value['path']  ):
																	# we set "bytes": None to not duplicate the data if they're already available locally
																	if value["path"].endswith('pcm'  ):
																								# "PCM" only has raw audio bytes
																								if value.get('sampling_rate'  ) is None:
																															# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																															raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object'  )
																								if value.get('bytes'  ):
																															# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																															SCREAMING_SNAKE_CASE_	:							str											=np.frombuffer(value['bytes']    ,							dtype=np.intaa  ).astype(np.floataa  ) / 32_767
																								else:
																															SCREAMING_SNAKE_CASE_	:							Dict											=np.memmap(value['path']    ,							dtype='h'    ,							mode='r'  ).astype(np.floataa  ) / 32_767

																								SCREAMING_SNAKE_CASE_	:							Dict											=BytesIO(bytes()  )
																								sf.write(snake_case_    ,							snake_case_    ,							value['sampling_rate']    ,							format='wav'  )
																								return {"bytes": buffer.getvalue(), "path": None}
																	else:
																								return {"bytes": None, "path": value.get('path'  )}
										elif value.get('bytes'  ) is not None or value.get('path'  ) is not None:
																	# store the audio bytes, and path is used to infer the audio format using the file extension
																	return {"bytes": value.get('bytes'  ), "path": value.get('path'  )}
										else:
																	raise ValueError(
																	    F"""An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}."""  )







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase    ,							__UpperCAmelCase = None  ):
										if not self.decode:
																	raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'  )

										SCREAMING_SNAKE_CASE_	:							Any											=(value["""path"""], BytesIO(value['bytes']  )) if value["""bytes"""] is not None else (value["""path"""], None)
										if path is None and file is None:
																	raise ValueError(F"""An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}."""  )

										try:
																	import librosa
																	import soundfile as sf
										except ImportError as err:
																	raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.'  ) from err

										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=xsplitext(snake_case_  )[1][1:].lower() if path is not None else None
										if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
																	raise RuntimeError(
																	    'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
																	    'You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. '  )
										elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
																	raise RuntimeError(
																	    'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
																	    'You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. '  )

										if file is None:
																	SCREAMING_SNAKE_CASE_	:							Dict											=token_per_repo_id or {}
																	SCREAMING_SNAKE_CASE_	:							str											=path.split('::'  )[-1]
																	try:
																								SCREAMING_SNAKE_CASE_	:							Any											=string_to_dict(snake_case_    ,							config.HUB_DATASETS_URL  )["""repo_id"""]
																								SCREAMING_SNAKE_CASE_	:							List[str]											=token_per_repo_id[repo_id]
																	except (ValueError, KeyError):
																								SCREAMING_SNAKE_CASE_	:							Any											=None

																	with xopen(snake_case_    ,							'rb'    ,							use_auth_token=snake_case_  ) as f:
																								SCREAMING_SNAKE_CASE_	:							List[Any]											=sf.read(snake_case_  )

										else:
																	SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=sf.read(snake_case_  )

										SCREAMING_SNAKE_CASE_	:							List[str]											=array.T
										if self.mono:
																	SCREAMING_SNAKE_CASE_	:							int											=librosa.to_mono(snake_case_  )
										if self.sampling_rate and self.sampling_rate != sampling_rate:
																	SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=librosa.resample(snake_case_    ,							orig_sr=snake_case_    ,							target_sr=self.sampling_rate  )
																	SCREAMING_SNAKE_CASE_	:							Dict											=self.sampling_rate

										return {"path": path, "array": array, "sampling_rate": sampling_rate}







			def      __lowerCamelCase    (	self  ):
										from .features import Value

										if self.decode:
																	raise ValueError('Cannot flatten a decoded Audio feature.'  )
										return {
										    "bytes": Value('binary'  ),
										    "path": Value('string'  ),
										}







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase  ):
										if pa.types.is_string(storage.type  ):
																	SCREAMING_SNAKE_CASE_	:							Optional[Any]											=pa.array([None] * len(snake_case_  )    ,							type=pa.binary()  )
																	SCREAMING_SNAKE_CASE_	:							List[str]											=pa.StructArray.from_arrays([bytes_array, storage]    ,							['bytes', 'path']    ,							mask=storage.is_null()  )
										elif pa.types.is_binary(storage.type  ):
																	SCREAMING_SNAKE_CASE_	:							str											=pa.array([None] * len(snake_case_  )    ,							type=pa.string()  )
																	SCREAMING_SNAKE_CASE_	:							List[Any]											=pa.StructArray.from_arrays([storage, path_array]    ,							['bytes', 'path']    ,							mask=storage.is_null()  )
										elif pa.types.is_struct(storage.type  ) and storage.type.get_all_field_indices('array'  ):
																	SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=pa.array([Audio().encode_example(snake_case_  ) if x is not None else None for x in storage.to_pylist()]  )
										elif pa.types.is_struct(storage.type  ):
																	if storage.type.get_field_index('bytes'  ) >= 0:
																								SCREAMING_SNAKE_CASE_	:							Optional[Any]											=storage.field('bytes'  )
																	else:
																								SCREAMING_SNAKE_CASE_	:							str											=pa.array([None] * len(snake_case_  )    ,							type=pa.binary()  )
																	if storage.type.get_field_index('path'  ) >= 0:
																								SCREAMING_SNAKE_CASE_	:							List[str]											=storage.field('path'  )
																	else:
																								SCREAMING_SNAKE_CASE_	:							int											=pa.array([None] * len(snake_case_  )    ,							type=pa.string()  )
																	SCREAMING_SNAKE_CASE_	:							Dict											=pa.StructArray.from_arrays([bytes_array, path_array]    ,							['bytes', 'path']    ,							mask=storage.is_null()  )
										return array_cast(snake_case_    ,							self.pa_type  )







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase  ):

										@no_op_if_value_is_null
										def path_to_bytes(__UpperCAmelCase  ):
																	with xopen(snake_case_    ,							'rb'  ) as f:
																								SCREAMING_SNAKE_CASE_	:							Optional[int]											=f.read()
																	return bytes_

										SCREAMING_SNAKE_CASE_	:							int											=pa.array(
										    [
										        (path_to_bytes(x['path']  ) if x['bytes'] is None else x['bytes']) if x is not None else None
										        for x in storage.to_pylist()
										    ]    ,							type=pa.binary()    ,							)
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=pa.array(
										    [os.path.basename(snake_case_  ) if path is not None else None for path in storage.field('path'  ).to_pylist()]    ,							type=pa.string()    ,							)
										SCREAMING_SNAKE_CASE_	:							Optional[int]											=pa.StructArray.from_arrays([bytes_array, path_array]    ,							['bytes', 'path']    ,							mask=bytes_array.is_null()  )
										return array_cast(snake_case_    ,							self.pa_type  )







import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase_    :			int    =	16
lowerCamelCase_    :			str    =	32







def 							A__       (    lowerCamelCase    )	->						int:
				return int(x / 2**20    )

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __enter__(							self			:							int   ):
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							UpperCamelCase_:				str						=    torch.cuda.memory_allocated()
							return self




			def __exit__(							self			:							List[Any]    ,						*snake_case_			:							Union[str, Any]   ):
							gc.collect()
							torch.cuda.empty_cache()
							UpperCamelCase_:				List[str]						=    torch.cuda.memory_allocated()
							UpperCamelCase_:				int						=    torch.cuda.max_memory_allocated()
							UpperCamelCase_:				Optional[int]						=    bamb(self.end - self.begin   )
							UpperCamelCase_:				Tuple						=    bamb(self.peak - self.begin   )
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 							A__       (    lowerCamelCase  ,     lowerCamelCase = 16  ,     lowerCamelCase = "bert-base-cased"  ,     lowerCamelCase = 3_20  ,     lowerCamelCase = 1_60  ,     )	->						Dict:
				UpperCamelCase_:				str						=    AutoTokenizer.from_pretrained(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    load_dataset(
				    """glue"""  ,     """mrpc"""  ,     split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''}    )

				def tokenize_function(lowerCamelCase    ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:				Optional[Any]						=    tokenizer(examples["""sentence1"""]  ,     examples["""sentence2"""]  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				UpperCamelCase_:				Dict						=    datasets.map(
				    lowerCamelCase  ,     batched=lowerCamelCase  ,     remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,     load_from_cache_file=lowerCamelCase    )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCamelCase_:				int						=    tokenized_datasets.rename_column("""label"""  ,     """labels"""    )

				def collate_fn(lowerCamelCase    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowerCamelCase  ,     padding="""max_length"""  ,     max_length=1_28  ,     return_tensors="""pt"""    )
								return tokenizer.pad(lowerCamelCase  ,     padding="""longest"""  ,     return_tensors="""pt"""    )

				# Instantiate dataloaders.
				UpperCamelCase_:				List[str]						=    DataLoader(
				    tokenized_datasets["""train"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    DataLoader(
				    tokenized_datasets["""validation"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				return train_dataloader, eval_dataloader







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				# Initialize accelerator
				UpperCamelCase_:				Any						=    Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase_:				str						=    config["""lr"""]
				UpperCamelCase_:				Any						=    int(config["""num_epochs"""]    )
				UpperCamelCase_:				Tuple						=    int(config["""seed"""]    )
				UpperCamelCase_:				Dict						=    int(config["""batch_size"""]    )
				UpperCamelCase_:				Dict						=    args.model_name_or_path

				set_seed(lowerCamelCase    )
				UpperCamelCase_, UpperCamelCase_:				int						=    get_dataloaders(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     args.n_train  ,     args.n_val    )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase_:				Any						=    AutoModelForSequenceClassification.from_pretrained(lowerCamelCase  ,     return_dict=lowerCamelCase    )

				# Instantiate optimizer
				UpperCamelCase_:				Tuple						=    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				UpperCamelCase_:				List[Any]						=    optimizer_cls(params=model.parameters()  ,     lr=lowerCamelCase    )

				if accelerator.state.deepspeed_plugin is not None:
								UpperCamelCase_:				Any						=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								UpperCamelCase_:				List[Any]						=    1
				UpperCamelCase_:				Optional[int]						=    (len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								UpperCamelCase_:				List[Any]						=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase  ,     num_warmup_steps=0  ,     num_training_steps=lowerCamelCase  ,     )
				else:
								UpperCamelCase_:				Dict						=    DummyScheduler(lowerCamelCase  ,     total_num_steps=lowerCamelCase  ,     warmup_num_steps=0    )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    accelerator.prepare(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				# We need to keep track of how many total steps we have iterated over
				UpperCamelCase_:				str						=    0
				# We also need to keep track of the stating epoch so files are named properly
				UpperCamelCase_:				Dict						=    0

				# Now we train the model
				UpperCamelCase_:				Dict						=    {}
				for epoch in range(lowerCamelCase  ,     lowerCamelCase    ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCamelCase    ):
																UpperCamelCase_:				Union[str, Any]						=    model(**lowerCamelCase    )
																UpperCamelCase_:				str						=    outputs.loss
																UpperCamelCase_:				List[str]						=    loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase    )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin    )    )    )
								accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used    )    )
								accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked    )    )
								accelerator.print(
								    """Total Peak Memory consumed during the train (max): {}""".format(
								        tracemalloc.peaked + bamb(tracemalloc.begin    )    )    )
								UpperCamelCase_:				Optional[Any]						=    tracemalloc.peaked + bamb(tracemalloc.begin    )
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir  ,     """peak_memory_utilization.json"""    )  ,     """w"""    ) as f:
												json.dump(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    )	->						Optional[Any]:
				UpperCamelCase_:				Any						=    argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
				parser.add_argument(
				    """--model_name_or_path"""  ,     type=lowerCamelCase  ,     default="""bert-base-cased"""  ,     help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,     required=lowerCamelCase  ,     )
				parser.add_argument(
				    """--output_dir"""  ,     type=lowerCamelCase  ,     default="""."""  ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""  ,     )
				parser.add_argument(
				    """--peak_memory_upper_bound"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""  ,     )
				parser.add_argument(
				    """--n_train"""  ,     type=lowerCamelCase  ,     default=3_20  ,     help="""Number of training examples to use."""  ,     )
				parser.add_argument(
				    """--n_val"""  ,     type=lowerCamelCase  ,     default=1_60  ,     help="""Number of validation examples to use."""  ,     )
				parser.add_argument(
				    """--num_epochs"""  ,     type=lowerCamelCase  ,     default=1  ,     help="""Number of train epochs."""  ,     )
				UpperCamelCase_:				Optional[int]						=    parser.parse_args()
				UpperCamelCase_:				Union[str, Any]						=    {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()






